56 results about "CarSim" patented technology

The invention discloses an automobile active anti-collision automatic lane change control system and an operating method thereof. The system comprises a real-time driving safety state judging module and an automatic lane change control module which are connected with each other; the real-time driving safety state judging module comprises a critical braking distance calculation module and a safety state judging module unit; the automatic lane change control module comprises a lateral dynamics and CarSim vehicle dynamics model establishing module, an expected yaw velocity acquiring module and a design terminal sliding formwork lane change controller module. The method includes two steps of driving safety state judgment and automatic lane change control. The system and method adopt pedestrians in front as the study objects of active anti-collision, whether driving is in the dangerous state or not can be judged by calculating the critical braking distance and corresponded automatic control is performed, the smoothness and operating stability during lane change are guaranteed, and safety of the pedestrians in front is guaranteed.

Disclosed is a yaw stability control method for representing nonlinear characteristics of an automobile. The method is characterized in that a reference model, a tire lateral force and cornering stiffness processor, an MPC and a Carsim automobile model. The reference model is used for determining the expected yaw velocity of the automobile; the tire lateral force and cornering stiffness processoris used for determining the side slip angle, lateral force and cornering stiffness of a tire; the Carsim automobile model is used for outputting actual motion state information of the automobile, andthe information comprises the longitudinal velocity, yaw velocity, centroid slip angle and road adhesion coefficient of the automobile; the MPC selects a prediction model according to the cornering stiffness of the tire, combines the expected yaw velocity of the automobile and the actual motion state information of the automobile to optimally calculate the front-wheel additional rotation angle ofthe automobile, superimposes the front-wheel additional rotation angle with a front-wheel rotation angle generated according to steering input of a driver and outputs a superimposition result to the Carsim automobile model to achieve yaw stability control over the automobile.

The invention discloses a multi-camera in-loop simulation test method and system for intelligent driving. Carsim software is arranged in a computer for simulating an experiment car and a traffic environment, and a camera is used for collecting video image data; the video image data collected by different cameras are respectively subjected to data correction; the image data corrected by different cameras are respectively perceived; the image data perceived by different cameras are fused; a driving behavior decision module analyzes a fused perception result to finish the driving decision work; adecision signal is transformed into a control signal of the experiment car; and the experiment car is conducted to drive in a traffic simulation environment according to the received control signal.According to the multi-camera in-loop simulation test method and system for intelligent driving in the invention, when a plurality of cameras are used for data collection, the data processing resultsof the plurality of cameras are fused through algorithm model optimization, a unified and effective target detection result is produced, and the intelligent driving technique is perfectly performed.

The invention discloses a three-dimensional evaluation and driving safety analysis method for a road surface accumulated water rut. Firstly, using high-precision road surface three-dimensional laser point cloud data to ensure the accuracy of driving safety evaluation; secondly, a continuous rut section can be obtained based on the three-dimensional reconstruction, the road is divided into two parts of drying and accumulated water according to precipitation conditions, the accumulation water area is divided into a plurality of areas, and the adhesion coefficient of each area are calculated fromaverage accumulated water depth of each area; finally, constructing a whole vehicle model by using the Carsim software, and importing the road segment rut data into the Carsim software, setting adhesion coefficients of different areas for simulation tests at different speeds, and finally performing vehicle driving safety analysis evaluation according to the lateral offset and the yaw angle of thevehicle.

The invention discloses an electric vehicle antilock control method based on linear time varying. The electric vehicle antilock control method is characterized by comprising an expectation slip rate module, a tire data processor, an MPC controller, a CarSim vehicle model, a slip rate calculating module and a braking torque distribution module. The expectation slip rate module is used for determining the expected slip rate, and the tire data processor is used for determining tire longitudinal force and tire longitudinal rigidity; the MPC controller optimizes and solves braking torque of four wheels according to the longitudinal speed and the slip rate of a current vehicle in combination with the expected slip rate; the CarSim vehicle model is used for outputting actual motion state information of the vehicle, and the actual motion state information comprises the longitudinal speed and the vehicle wheel rotation speed of the vehicle; the slip rate calculating module calculates the slip rate according to the output longitudinal speed and the vehicle wheel rotation speed of the vehicle; the front wheel braking torque is achieved by a hydraulic braking system, and the braking torque distribution module distributes braking torque of rear wheels, and determines hydraulic braking torque and motor regeneration braking torque to be input to the CarSim vehicle model.

A path tracking control method suitable for a high-speed limiting working condition is characterized in that the method comprises an expected path calculation module, a tire model linearization module, an MPC controller and a CarSim automobile model; the expected path calculation module is used for calculating an expected lateral displacement and a yaw angle of the automobile; the tire model linearization module is used for realizing linear approximation of nonlinear tire force in a prediction time domain; the CarSim automobile model is used for outputting the actual state quantity of the automobile, wherein the actual state quantity comprises a longitudinal speed, a lateral speed, a yaw velocity, the yaw angle and the lateral displacement of the automobile; and the MPC controller is usedfor solving a front wheel turning angle of the automobile according to the expected lateral displacement, the expected yaw angle and the actual state quantity of the automobile, inputting the front wheel turning angle to the CarSim automobile model, and controlling the automobile to realize path tracking control.

The invention discloses an automobile transverse and longitudinal stability cooperative control method under an extreme working condition. The method comprises the steps: obtaining a four-wheel-hub-motor-driven electric automobile model through simulation software CarSim; designing a two-degree-of-freedom reference model, and deriving expected values of a lateral speed and a yaw velocity of the automobile through a two-degree-of-freedom reference model; adopting a double-layer control structure to reduce the solving complexity, employing an NMPC controller for the upper layer to ensure that transverse and longitudinal stability of the automobile is used as a control target, and carrying out optimized solution by considering transverse and longitudinal safety constraints to obtain virtual control variables, namely expected values of a tire slip rate and a slip angle; and finally, acquiring an additional torque acting on the hub motor according to the deviation between the actual slip rate and slip angle of the tire and the expected value given by the upper layer. Therefore, the transverse and longitudinal stability of the vehicle is ensured.

The invention discloses an intelligent vehicle real-time trajectory planning method based on nonlinear model predictive control; the method comprises the following steps of by combining a linear two-degree-of-freedom model of a vehicle and a kinematic equation of the vehicle under geodetic coordinates, establishing a nonlinear dynamics equation of a controlled object; discretizing the establishednonlinear dynamics model by using a time interval Ts to obtain a state prediction equation of the system in the prediction time domain; constructing NMPC problem containing a terminal constraint by introducing a cost function and the constraint of the control problem; solving the nonlinear optimal control problem through a tool box, and obtaining the optimal control sequence, and supplying the first component of the optimal control sequence to the controlled object; and establishing a Simulink and Carsim combined simulation platform for experimental verification.

The invention discloses an in-loop simulation platform for real time hardware of a body electronic stability system (ESP). A virtual vehicle model and a simulation environment are used for simulating a real vehicle and a driving condition; an industrial personal computer and a PXI real-time system are used for transmitting pressure signals of all wheel cylinders to a CarSim vehicle model during ESP working; a driving state of a vehicle is displayed in real time by animation, thereby realizing development and testing of an ESP controller. Meanwhile, a real vehicle testing effect is realized in a laboratory environment. With the PXI real-time system, the real-time performance of the whole simulation process is guaranteed, so that the simulation precision is improved substantially and application of the virtual testing field to the real-time monitoring testing process is realized. In addition, the invention also disclose an in-loop simulation method for real time hardware of a body electronic stability system.

Provided is an anti-lock control method for an electrical automobile based on model prediction. The method is characterized by comprising a desired slip ratio module, a tire data processor, an MPC controller, a Carsim automobile model, a slip ratio calculation module and a braking torque distribution module. The desired slip rate module is used for determining the desired slip rate, and the tire data processor is used for determining tire longitudinal force and tire longitudinal stiffness; the MPC controller is used for optimization solution to obtain braking torque of four wheels according tothe longitudinal speed and slip ratio of a current automobile, the braking torque of front wheels is directly input to the CarSim automobile model, and the braking torque of rear wheels is input to the braking torque distribution module; the CarSim automobile model is used for outputting actual motion status information of the automobile, and the actual motion status information includes the longitudinal speed and wheel speed of the automobile; the slip ratio calculation module is used for calculating the slip ratio according to the output longitudinal speed and wheel speed of the automobile;the braking torque distribution module is used for distributing the braking torque of the rear wheels, determining hydraulic braking torque and motor regenerative braking torque, and inputting to theCarSim automobile model.

The invention discloses a Carsim simulation based calculation method for a speed limit value of a highway circular curve section. The method comprises the following steps: respectively establishing ayaw rate safe boundary calculation model and a front wheel side slip angle absolute value mean safe boundary calculation model, and obtaining a yaw rate safe boundary and a front wheel side slip angleabsolute value mean safe boundary by calculation based on the models; establishing a driver control-car-3D highway coupling simulation model in car dynamics simulation software Carsim, gradually increasing simulation speed in the simulation model with designed car speed as initial simulation speed, obtaining yaw rate and front wheel side slip angle absolute value mean which are fed back by the car during running on the highway; when any value of the fed-back yaw rate and front wheel side slip angle absolute value mean reaches the safe boundary, determining the simulation speed at the time asthe speed limit value corresponding to the highway circular curve section. The method can accurately reflect the influence of highway 3D alignment and surface attachment conditions on lateral movementof the car, and driving safety under different weather conditions and geometric parameters of circular curve sections can be improved.

The invention discloses an automobile path tracking control method based on a corner optimization sequence. The method is characterized in that the method employs an MPC path tracking controller, a tire slip angle estimation module, a CarSim automobile model, and a tire model linearization module. The tire slip angle estimation module is used for estimating a tire slip angle sequence in a prediction time domain, the tire model linearization module is used for carrying out linear processing on nonlinear tire force in the prediction time domain, and the MPC path tracking controller optimizes andsolves a front wheel turning angle sequence of an automobile and inputs the front wheel turning angle sequence to the CarSim automobile model to realize path tracking control.

The invention relates to a driving wheel anti-skid and torque optimization fused electric vehicle stability control method, which is characterized in that the method comprises a reference model module, a fuzzy controller, a driving anti-slip controller, a torque optimization controller and a CarSim vehicle model, wherein the reference model module calculates a reference yaw velocity and a side slip angle according to driver input and a vehicle longitudinal speed; the CarSim vehicle model outputs the actual state quantity of the vehicle; the fuzzy controller calculates the additional yaw moment of the vehicle according to the reference yaw velocity, the reference side slip angle and the actual state quantity of the vehicle; and the torque optimization controller outputs the required torque of two rear wheel hub motors according to the required torque of the whole vehicle, the additional yawing torque, the tire lateral force, the vertical force and the rotating speed of the two rear wheels, the anti-slip controller is driven to control the slip rate of the left driving wheel and the right driving wheel so as to enable the slip rate to be kept near the optimal slip rate, and the two controllers complement each other, so that the stability control of the vehicle under the extreme road condition is realized.

An automobile stability control method based on tire equal backup capacity is characterized in that a reference model module, a tire data processing module, an MPC controller and a CarSim automobile model are included; the reference model module is used for calculating a reference yaw velocity and a side slip angle; the tire data processing module is used for calculating tire lateral force and tire state rigidity; the CarSim automobile model is used for outputting the actual state quantity of an automobile; and the MPC controller optimizes a front wheel rotation angle and braking torques of four wheels and inputs the front wheel rotation angle and the braking torques to the CarSim automobile model to realize reasonable distribution of steering and braking control rights.

The invention provides an all-electric drive distributed unmanned vehicle motion simulation platform and a design method thereof. According to the method, a whole vehicle boundary dimension, aerodynamics, suspension and tire system parameter model is completed in Carsim; meanwhile, the driving end, the steering end and the braking end of the Carsim traditional automobile model are disconnected; adriving system model, a steering system model and a braking system model are established by Simulink; exchange is carried out through an external data interface in Carsim software, so that a vehicle model in Carsim software obtains independent transient parameters of driving torque, braking torque and a steering angle, a driver control model and an algorithm control decision model are establishedin Simulink, and establishment of a distributed unmanned vehicle simulation platform is achieved; the unmanned vehicle simulation platform has the advantage of independent driving / steering / braking, and six steering modes such as two-wheel steering, four-wheel steering, crabbing and pivot steering can be realized based on the steering kinematics model established by the platform, so that the motionmode of the unmanned vehicle can be comprehensively simulated.

The invention belongs to the technical field of electric vehicles, and discloses an electric vehicle brake system model and a building method thereof, and the building method of the electric vehicle brake system model comprises the following steps: building a simulation model of an electric vehicle AEB; creating an interface of the CarSim software and an interface of the Simulink software; creating a braking system model based on Simulink; and verifying the simulation model. Simulation experiment analysis results show that the invention can basically realize collision avoidance in an AEB test scene specified in C-NCAP, and the control strategy is greatly improved compared with a classic algorithm in five indexes of speed reduction rate, maximum deceleration, deceleration change rate, minimum distance between two vehicles and early warning time. And the safety of the automobile and the comfort of a driver are improved, and the road surface with a low road surface adhesion coefficient can be well adapted.

The invention provides a simulation method for the minimum turning radius of a four-wheel steering belt differential vehicle. The method comprises the steps that: a vehicle dynamics model is established in a steering system in Carsim according to existing parameters, and the accuracy of necessary parameters is ensured; a Python interface in Carsim is defined, the Python interface comprises a path configuration file and input variables and output variables of a model, and an observation variable of a tire mark center of a wheel on the outermost side is established; and by writing a corresponding py control file, minimum turning radius simulation meeting requirements on a vehicle model is performed in Carsim. The Carsim vehicle dynamics model, the four-wheel steering, the torque control strategy and the Python interface control program are effectively connected together, and simulation of the four-wheel steering and the control strategy is effectively achieved.

The invention discloses a vehicle path tracking method based on improved Stanley control, and the method comprises the steps: carrying out the modeling of a path tracking method according to a Stanley control algorithm based on a two-degree-of-freedom vehicle motion model; an F Stanley algorithm path tracking controller is designed by using the model, a fuzzy controller is designed to continuously adjust and optimize gain parameters of the Stanley controller, transverse control of the unmanned vehicle is realized by improving the controller, and longitudinal control is controlled through a CarSim vehicle model; and performing simulation analysis on the path tracking controller, and evaluating the universality of the improved controller under various trajectories and curvatures. The application range of the vehicle speed is widened, the vehicle path tracking precision and the operation stability are improved, the self-adaptive controller is provided, parameters are automatically adjusted according to the vehicle state and the path course, and the vehicle path tracking accuracy is improved. On the basis of a traditional Stanley controller, gain parameters of the unmanned vehicle trajectory tracking controller are optimized through a fuzzy algorithm, and the path tracking precision is improved.

The invention discloses an automobile active suspension control method based on a parameter real-time adjustable PID (Proportion Integration Differentiation) controller, which specifically comprises the following steps of establishing a whole automobile dynamic model in carsim, establishing various simulation working conditions for testing automobile driving performance, and setting input and output variables relative to simulink; firstly, enabling a carsim to output a simulation result of an automobile under the current working condition to a PID controller with real-time adjustable parameters, enabling the PID controller to determine the damping force of a shock absorber according to a certain control logic and transmit the damping force back to the carsim through simulink, and enabling the carsim to calculate all indexes of the automobile again and output the indexes to the simulink; judging whether each index is in an expected range or not by the simulink, and if a certain index is not in the expected range, adjusting the damping force of the shock absorber by the PID controller and inputting the damping force to the carsim again, until all indexes simulated by carsim are in an expected range. According to the method, a carsim andmatlab\simulink joint simulation mode is adopted, a unified simulink simulation framework is adopted, multi-scene and multi-target control of the vehicle is carried out, and a remarkable control effect is achieved.

The invention discloses an automatic driving automobile simulation system, which comprises the following steps: environment simulation: providing a traffic environment by using Carla software, and simulating a surrounding environment possibly occurring in automobile driving; vehicle simulation: using Carsim software to provide a vehicle capable of being controlled by an automatic driving algorithm; vehicle control: an automatic driving algorithm obtains information around the vehicle from the Carla software through a sensor, and after the automatic driving algorithm obtains the information, a calculated decision is issued to the Carsim software; and information feedback: the Carsim software obtains the state information of the vehicle and transmits the state information to the automatic driving algorithm, and the automatic driving algorithm feeds the information back to the Carla software. Traffic environments, including roads, vehicles, pedestrians, traffic signs, various times, weather and the like, which are close to reality can be simulated in computer software, a simulation environment is provided for development of an automatic driving algorithm, the algorithm verification time is shortened, and meanwhile, the cost is greatly saved.

The invention discloses a time-delay feedback neural network-based vehicle dynamics prediction model, a training data acquisition method and a training method, provides a vehicle dynamics virtual and actual data set acquisition method under a multi-road condition, and lays a data foundation for vehicle dynamics model establishment. The method comprises the following steps: firstly, selectively adding different fidelity models based on vehicle nonlinear dynamics to obtain a low-fidelity interpretable vehicle nonlinear dynamics model multi-time-step virtual data set with different complexity degrees; secondly, obtaining multi-time-step virtual data of the high-fidelity dynamics model through high-fidelity vehicle dynamics software CarSim; and finally, arranging an actual unmanned vehicle dynamics data acquisition device to obtain a vehicle dynamics real data set. The freedom degree selection range of the vehicle dynamics virtual data set is wide, the obtaining cost is low, the demand quantity of real vehicle data is reduced, the weight parameters of the model are optimized again through the vehicle dynamics real data set, and the accuracy of actual vehicle dynamics prediction response is improved.

The invention belongs to the technical field of four-wheel steering control, and discloses a four-wheel steering rolling time domain control method based on a Koopman operator, which comprises the following steps of: S1, establishing an ideal reference model in combination with adhesion conditions of a road surface and stability factors of a vehicle, and determining an expected yaw velocity gamma * and a side slip angle beta * through the ideal reference model; s2, collecting target data based on Carsim and Simulink joint simulation, wherein the target data is used for reflecting the relationship between the yaw velocity and the side slip angle and the front wheel rotation angle and the rear wheel rotation angle of the vehicle; raising the dimension of the vehicle four-wheel steering model to a high-dimensional space based on a Koopman operator theory and extended dynamic mode decomposition to obtain a vehicle four-wheel steering global linearization model; and s3, designing a linear rolling time domain controller according to the vehicle four-wheel steering global linearization model, wherein the controller takes an expected yaw velocity gamma * and a side slip angle beta * as reference inputs, takes a front wheel rotation angle and a rear wheel rotation angle of the vehicle as reference outputs, and acts the reference outputs on the vehicle system.

The invention provides an ultrasonic radar in-loop automatic driving test method based on Gaussian clustering, and the method comprises the steps: determining a dangerous scene when an ultrasonic radar works through a Gaussian mixture model, building the dangerous scene through a virtual simulation platform such as Prescan, introducing a real ultrasonic radar into a test loop, and improving the test reliability. Echo signals received by the ultrasonic radar are generated by an echo simulator, Carsim software and Simulink are fused into a control algorithm of the vehicle to jointly carry out simulation testing, and finally a testing result is obtained to judge the reliability and the safety of the control algorithm of the automatic driving vehicle. According to the method, the defects of inconvenience and uneconomy caused by actual field testing are overcome, the cost of manpower and material resources is effectively reduced, and the testing efficiency is greatly improved.

The invention provides a vehicle joint simulation method and system carrying ACC and ESC. The method depends on a dynamics simulation platform of CarSim software, and vehicle dynamics models of ACC and ESC are integrated; secondly, a multi-dimensional traffic scene is built based on PreScan, various typical road networks are fused, variable illumination (day and night / rain and snow) and intelligent road facilities are integrated, and environment sensing data are generated through a camera, a millimeter wave radar and a laser radar multi-mode sensor; and finally, a simulation system is built in Simulink, real-time data interaction with vehicle dynamics and environmental perception is realized through an S-Function interface, and system verification including a decision planning algorithm and a sensor fusion model is built. According to the invention, a basis example is provided for verifying the joint effect between the ADAS, and through the combined use of a plurality of simulation software, the defects between the simulation software are effectively avoided, so that the data is more real and credible, and meanwhile, the safety problem and the economic cost of a real vehicle experiment are reduced.

The invention relates to the technical field of automobile chassis function safety, and discloses a steering function safety controllability grading evaluation method based on simulation and clustering analysis, and the method comprises the following steps: S1, defining vehicle parameters based on Carsim; s2, building a driver model; s3, a fault injection module is set up in the Simulink; s4, a vehicle operation scene is built, joint simulation is carried out based on Carsim and Simulink, and lateral deviation data is obtained; s5, controlling the driver model to control vehicle running states in different failure forms, traversing different vehicle initial states and different driver models, and running joint simulation to obtain multiple groups of test data; s6, extracting target data from the test data; s7, performing normalization processing on the target data; and S8, performing clustering analysis, and classifying data clusters formed by clustering to obtain different controllability levels. According to the method, the controllability can be comprehensively evaluated, the evaluation result is reliable, objective and accurate, and the method has actual reference value.

The invention discloses a method, platform and storage medium for verifying the expected functional safety of an automatic driving algorithm, which includes: establishing an automatic driving function algorithm in Matlab / Simulink software, and establishing a corresponding virtual version of the automatic driving function algorithm through virtual scene modeling software. Autonomous driving scenario; a real vehicle dynamics model is established by Carsim and run in the hardware dSPACE Scalexio system; the automatic driving function algorithm established in the Matlab / Simulink software is quickly prototyped in the hardware dSPACE MicroAutoBox; the above closed-loop control process forms a Autonomous driving HIL test system, in which virtual modeling is performed based on SOTIF hazardous scenarios, and the autonomous driving function algorithm in dSPACE MicroAutoBox is verified by SOTIF MIL and HIL.

The invention relates to an automobile lateral stability improving method based on stable state prediction. The method is characterized in that an expected yaw velocity and side slip angle calculationmodule, a stability judgment module, a model prediction controller and a CarSim vehicle model are included; the expected yaw velocity and side slip angle calculation module is used for determining the expected automobile yaw velocity and side slip angle; the stability judgment module is used for determining the automobile stable state trend; the CarSim vehicle model is used for outputting theautomobile actual state quantity, and the actual state quantity comprises the longitudinal velocity, the yaw velocity and the side slip angle of the automobile; and the model prediction controller optimizes and solves the front wheel turning angle of the automobile and inputs the front wheel turning angle to the CarSim vehicle model, so that the yaw velocity and the side slip angle of the automobile are tracked and controlled.

The invention discloses a method for three-dimensional evaluation of road surface water ruts and driving safety analysis. Firstly, high-precision three-dimensional laser point cloud data on the road surface is used to ensure the accuracy of driving safety evaluation; secondly, on the basis of three-dimensional reconstruction, the method can be obtained Continuous rutting section, divide the road into dry and waterlogged parts according to the precipitation situation, divide the waterlogged area into several areas, and calculate the adhesion coefficient of each area from the average water accumulation depth of each area; finally use Carsim software to construct The vehicle model, and the rutting data of road sections are imported into Carsim software, and the adhesion coefficients of different areas are set to carry out simulation tests at different speeds. Finally, the vehicle driving safety is analyzed and evaluated according to the vehicle lateral offset and yaw angle.

The invention discloses an electric vehicle antilock control method based on linear time varying. The electric vehicle antilock control method is characterized by comprising an expectation slip rate module, a tire data processor, an MPC controller, a CarSim vehicle model, a slip rate calculating module and a braking torque distribution module. The expectation slip rate module is used for determining the expected slip rate, and the tire data processor is used for determining tire longitudinal force and tire longitudinal rigidity; the MPC controller optimizes and solves braking torque of four wheels according to the longitudinal speed and the slip rate of a current vehicle in combination with the expected slip rate; the CarSim vehicle model is used for outputting actual motion state information of the vehicle, and the actual motion state information comprises the longitudinal speed and the vehicle wheel rotation speed of the vehicle; the slip rate calculating module calculates the slip rate according to the output longitudinal speed and the vehicle wheel rotation speed of the vehicle; the front wheel braking torque is achieved by a hydraulic braking system, and the braking torque distribution module distributes braking torque of rear wheels, and determines hydraulic braking torque and motor regeneration braking torque to be input to the CarSim vehicle model.